I've currently decided to get a second bachelor's in Statistics over 1.5 years after finishing my first bachelor's in math. Both of these degrees are debt free and follow a passion of mine. The next goal is to work in industry for about three years at my parent's home while taking one class a semester in Computer Science online to save for a master's in Statistics with Thesis where I hope to take some really advanced courses over two years.

The big idea is that I could grab courses like Categorical Data Analysis, Multivariate Statistics, Nonparametric stats (and maybe substitute mathematical stats 1 and 2 with graduate Measure Theory) etc during my second B.S. and then focus on the really advanced statistics courses and math courses during my masters like advanced graduate topology, experimental design and bayesian analysis

Then I would start a PhD in say Computer Science focused on Machine Learning to develop cutting edge statistical tools based on theoretical principles in Statistics, computational topology, measure theory etc.

Would this be too much education? I want to do more than just take classes on various statistical methods before a PhD.

Edit: I should add that the answer I received from r/datascience was to go straight into a Masters program. But since I can't afford that yet, I guess the answer in my circumstances is that I can do a second B.S. in Stats or Comp Sci if I'm making good money near my parent's home?

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    What country do you live in? The answer is radically different if you live in the US or another country where PhDs contain masters degrees within them. – Stella Biderman Oct 15 '17 at 19:57
  • I live in the US. Though I'm not too interested in maximizing my earning potential / minimizing lost time – Kulgurae Oct 15 '17 at 21:11
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    Why do you want to do this though. Your post doesn't say – Stella Biderman Oct 15 '17 at 21:34
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    I don't understand. Is the second undergraduate degree not going to cost you money? I would expect a second BS to cost as much as a Masters, if not more. Where are you going to school and who is paying for it? – Stella Biderman Oct 15 '17 at 22:07
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    That's an interesting (and exceptionally fortunate) situation. Why don't you want a masters from this university? No matter the quality of the education, admissions departments will look better upon a masters than an undergrad. You'll probably learn more from a masters too. This goes double for if there's a thesis option for a masters. 45 credits is a masters degree worth of credits – Stella Biderman Oct 15 '17 at 22:31

I would agree with the answer from r/datascience. Go straight to MS.

The main reason you cited for waiting on the MS was that you could not afford to pay cash for grad school and you didn't want a loan. Keep in mind that cash and loans are not the only two options. There is a significant amount of financial assistance available for grad school. E.g. you might be able to get a teaching assistantship (teach an undergrad class in exchange for tuition), research assistantship (work on a professor's research project in exchange for tuition), or a fellowship/scholarship. I got all three of these at various points in my program. I have a MS and a Ph D and I literally paid zero tuition for both of them. My wife and my sister both have MS and neither one paid any tuition.

Research assistantship is very common in technical fields where corporations sponsor research projects. E.g. Apple or Microsoft might give a CS professor $200k to study some challenging problem. The professor will spend that $200k to hire grad students to do the work. You work on the project part time, say 20 hours a week, and that pays your tuition. Now if you were talking about studying history or poetry or violin performance, then money is a lot harder to find. But Comp Sci, you have a good chance at finding some money.

The only reason I can think of to do a second BS before the MS would be if your BS was in such a completely unrelated area that you would be unlikely to get admitted to the graduate program. For example, if you had a BS in English Literature or Violin Performance, you would be unlikely to get into an graduate program in Statistic or Comp Science. But I don't think that applies here. With a Math degree (assuming you have good GPA / GRE scores) you should be able to get into a good Statistics or Computer Science program.

EDIT: Let me try to put this a different way. To a large degree, the value of a BS is less about the specific facts/skills that you learned, and more about proving to a potential employer or grad school that you are a hard worker (see Signaling theory). Opinions vary, but let's says it's 50% about what you learn, and 50% proving you're a hard worker. If you get a second BS, it's only worth half as much as the first, as you've already proven you can do work at the BS level. If you get an MS, you now prove to potential employers that you can do work at the MS level, which is worth a lot more than proving you can work at the BS level. Let's just say that an MS is worth twice as much as the first BS for this. Then an MS is worth four times as much as a second BS. And since it will take a similar amount of time, why waste your time getting the less valuable thing?

  • Would it hurt me if I still decided to do the second B.S. to take more advanced courses during my M.S.? – Kulgurae Oct 16 '17 at 0:13
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    @Kulgurae Most M.S. programs will have required courses at the "intro masters level." I don't think you can assume you'll take more advanced courses simply because you have a BS in stats. – Stella Biderman Oct 16 '17 at 1:06

As someone doing an interdisciplinary PhD (a mandatory element of my programme) in CompSci and Music, there is nothing interdisciplinary about the subjects you've already studied between maths, stats and CS. Now don't get me wrong, they all compliment each other very well, and judging by the fact you can even consider doing more than one bachelors, you're probably an extremely competent and bright student who would sail through a CS PhD. But to say any element of this is interdisciplinary is a bit of a farce. Stats, maths and CS is, in most universities housed under the same faculty. You wouldn't be able to say you're doing any combination of those three in my programme and justify it as interdisciplinary work. If you'd like to learn more about interdisciplinarity in research, the classic text to start with is Alan Repko's Interdisciplinary Research: Process and Theory. If you want to put it into practice, consider working with another faculty for your PhD outside of electronics, maths and computer science within your institution. There are many opportunities to apply Machine Learning techniques to any number of disciplines, so the limit is your imagination (and a good supervisor ;) ).

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